Association Between Quality of Discharge Teaching and Post-Discharge Coping Difficulty in Postoperative Lung Cancer Patients: A Chain Mediation Model
Bibliographic record
Abstract
The post-discharge coping difficulties experienced by patients can affect their quality of life and the occurrence of unplanned readmissions. This study aimed to explore the chain mediation effect of self-efficacy and readiness for hospital discharge between quality of discharge teaching and post-discharge coping difficulty among postoperative lung cancer patients. This study employed a cross-sectional design and surveyed 358 postoperative patients with lung cancer. Demographic and Disease-Related Data Questionnaire, Quality of Discharge Teaching Scale, General Self-Efficacy Scale, Readiness for Hospital Discharge Scale, and Post-Discharge Coping Difficulty Scale were used. A structural equation model was utilized to explore the mediation effects of self-efficacy and readiness for hospital discharge. The total score for post-discharge coping difficulty among postoperative lung cancer patients was 34.32 ± 10.00. Quality of discharge teaching not only directly negatively predicted post-discharge coping difficulty (β = −0.154, p < 0.05), but also indirectly affected it through the chain mediation effect of self-efficacy and readiness for hospital discharge (β = −0.040, p = 0.001). Healthcare providers should pay attention to postoperative lung cancer patients’ post-discharge coping difficulties and formulate targeted discharge teaching strategies to enhance patients’ self-efficacy and readiness for discharge to alleviate their post-discharge coping difficulties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".